Publications

Category:

2023

Generative Adversarial Network Synthesis for Improved Deep Learning Model Training of Alpine Plants with Fuzzy Structures

Praschl, C. , Kaiser, R., and Zwettler, G. (2023). Generative Adversarial Network Synthesis for Improved Deep Learning Model Training of
Alpine Plants with Fuzzy Structures. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications – VISAPP.

Abstract

Deep learning approaches are highly influenced by two factors, namely the complexity of the task and the size of the training data set. In terms of both, the extraction of features of low-stature alpine plants represents a challenging domain due to their fuzzy appearance, a great structural variety in plant organs and the high effort associated with acquiring high-quality training data for such plants. For this reason, this study proposes an approach for training deep learning models in the context of alpine vegetation based on a combination of real-world and artificial data synthesised using Generative Adversarial Networks. The evaluation of this approach indicates that synthetic data can be used to increase the size of training data sets. With this at hand, the results and robustness of deep learning models are demonstrated with a U-Net segmentation model. The evaluation is carried out using a cross-validation for three alpine plants, namely Soldanella pusilla, Gnaphalium supinum, and Euphrasia minima. Improved segmentation accuracy was achieved for the latter two species. Dice Scores of 24.16% vs 26.18% were quantified for Gnaphalium with 100 real-world training images. In the case of Euphrasia, Dice Scores improved from 33.56% to 42.96% using only 20 real-world training images.

2022

User Profile-Based Recommendation Engine Mitigating the Cold-Start Problem

Mayrhuber E., Krauss O., “User Profile-Based Recommendation Engine Mitigating the Cold-Start Problem” in Proceedings of International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), 2022.

Abstract

Recommendation systems can be used in many situations in daily life. Recommending people on social media networks, products in various online shops, music, or movies are only a few use cases of these systems. The cold start problem, when no information about a new or infrequent user is available, is challenging for recommendation systems. We deal with creating restaurant and category recommendations for restaurant visitors. Recommendations are generated with different metrics and technologies based on user profiles to make recommendations as individual as possible. We use k-Means and Mean-Shift for clustering users to build a base for recommendations generated using user-based and contentbased collaborative filtering methods. These suggestions consider the location of restaurants, the similarity between users and restaurants, and the ratings users give. We mitigate the cold-start problem by using matrix factorization and spatial information for users with few restaurant visits in the past. Recommendations are evaluated and adapted as a result of other user behavior to obtain better results. As a result, we can query recommendations via an Application Programming Interface (API), which consist of a mixture of location and user-based recommendation to please the users’ needs by combining exploration and exploitation.

A Comparison Of Relational, NoSQL and NewSQL Database Management Systems For The Persistence Of Time Series Data

Praschl C., Pritz S., Krauss O., Harrer M., “A Comparison Of Relational, NoSQL and NewSQL Database Management Systems For The Persistence Of Time Series Data” in Proceedings of International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), 2022.

Abstract

Time series data is created in a variety of application areas such as sensors in cars, smartwatches or IoT devices. This kind of data is often characterized by high resource demand due to the frequency the information is measured, with data points once a day, hour and even down to milliseconds. While real-time processing of such data is often sufficient, there are also many use cases, where batch processing and consequently the storage and managed access of measurements is required. For this reason, this work evaluates different database management systems in the context of storing time related data using different data models such as classical relational models, non-relational models using NoSQL database systems and the recently upcoming group of NewSQL databases. The evaluation shows that a highly optimized time series databases such as InfluxDB is able to outperform the other tested systems regarding write-throughput and RAM as well as disk utilization in a single server setup.

ProperBERT - Proactive Recognition of Offensive Phrasing for Effective Regulation

Clara Diesenreiter, Oliver Krauss, Simone Sandler, Andreas Stöckl, “ProperBERT – Proactive Recognition of Offensive Phrasing for Effective Regulation” in Proceedings of International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), 2022.

Abstract

This work discusses and contains content that may be offensive or unsettling. Hateful communication has always been part of human interaction, even before the advent of social media. Nowadays, offensive content is spreading faster and wider through digital communication channels. To help improve regulation of hate speech, we introduce ProperBERT, a fine-tuned BERT model for hate speech and offensive language detection specific to English. To ensure the portability of our model, five data sets from literature were combined to train ProperBERT. The pooled dataset contains racist, homophobic, misogynistic and generally offensive statements. Due to the variety of statements, which differ mainly in the target the hate is aimed at and the obviousness of the hate, a sufficiently robust model was trained. ProperBERT shows stability on data sets that have not been used for training, while remaining efficiently usable due to its compact size. By performing portability tests on data sets not used for fine-tuning, it is shown that fine-tuning on large scale and varied data leads to increased model portability.

Detecting Fake News and Performing Quality Ranking of German News Papers Using Machine Learning

Sandler Simone, Krauss Oliver, Diesenreiter Clara, Stöckl
Andreas, “Detecting Fake News and Performing Quality Ranking of German News Papers Using Machine Learning” in Proceedings of International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), 2022.

Abstract

Nowadays, news spread quickly, and it is not always clear to the reader whether an article is real or fake. Moreover, readers use only a few sources to read the news without knowing the quality of the source. This is due to a lack of up-to-date
news or media rankings. Machine learning models can be used to automatically detect fake news. In this work, a Passive-Aggressive-Classifier, a Random-Forest, and an LSTM network are trained to distinguish between fake and non-fake (real)
news. Moreover, these models are used to classify news sources according to the amount of possible Fake News they may spread. The models are tested on English and translated German articles. The best results for Fake News detection on English articles is reached with the Passive-Aggressive-Classifier. For automatic
news ranking of translated German articles, Random-Forest provides the best result. The correlation of Random-Forest with an actual news ranking reached 0.68. This shows that automated classification can be extended to languages other than English, using this approach. In the future, other machine learning models
and translators will be used to extend the approach.

Toward an Effective Community Energy Management by Using a Cluster Storage

A. Veichtlbauer, C. Praschl, L. Gaisberger, G. Steinmaurer and T. I. Strasser, “Toward an Effective Community Energy Management by Using a Cluster Storage,” in IEEE Access, vol. 10, pp. 112286-112306, 2022.

DOI: 10.1109/ACCESS.2022.3216298.

Abstract

The integration of renewable local energy generation in single households – turning the household into a “prosumer” – is an important way to support an ecological transition of the electric power system. However, due to the volatile and distributed nature of most renewable energy sources, the power system may face stability problems when integrating a large number of renewables. The paper at hand describes an approach to overcome these shortages in a two-fold manner: First, the effects of the installed renewables shall be limited locally to a group of households – a so-called “energy community”. To do so, all the participating households are using existing self-consumption optimization tools. However, when a household has excess energy which can not be consumed locally, this energy is shared among the other participating households by using a cluster storage device, thus enabling a community self-consumption before feeding into the low-voltage distribution grid. Second, the connected operator may request flexibility from the participating households. For that, additional loads or load sheds are triggered by the requesting grid operator, depending on the current situation in the grid. The households decide autonomously about the amount of granted flexibility, receiving respective financial incentives. This work introduces an energy management concept and a prototypical control infrastructure used for the aforementioned functionalities. In a number of simulations and field tests, the proposed approach was successfully evaluated. The article provides a comprehensive overview of the gained results and the conclusions derived from them.

Visual Change Detection in Multi-Temporal Transects of Alpine Plants

Pritz S., Praschl C., Kaiser R., Zwettler, G. “Visual Change Detection in Multi-Temporal Transects of Alpine Plants”. Proceedings of the 10th International Workshop on Simulation for Energy, Sustainable Development & Environment SESDE2022, Rom, Italy (2022).

Abstract

Due to the apparent effects of climate change on the Earth’s ecosystems, it is more important than ever to monitor flora and fauna in affected regions, e.g. mountain areas above the tree line. In the alpine ecosystem, and not just there, Vegetation plays a fundamental role and is the subject of this study. The work aims to develop algorithms for recognising small stature alpine plants from close range top view images. Ideally, automated assessment algorithms of the plant cover should objectively help scientists observe and interpret the state of the plant ecosystem over a long time series. Therefore, the aim in this respect was to derive visualisations that accurately describe plant growth and displacement (translocation). Additionally, recording changes in biodiversity was an intent. This work uses multi-temporal data comprising RGB images and multi-label masks to accomplish the aforementioned task. The evaluated methods involve mask comparison, optical flow estimation, detection of individual plants, and descriptive statistical analysis of image feature properties. Tests on the given data set show that all methods but the optical flow estimation have great potential. The mask comparison method captured plant growth and translocation most satisfactory. Individual plant detection and statistical analysis further helped to evaluate changes in biodiversity. When combined, the proposed methods give an immediate overview about relevant changes in the multi-temporal transects, which has not been done before for close-distance images of alpine plants.

Modelling shifting trends over time via topic analysis of text documents

Krauss O., Aschauer A., Stöckl A. “Modelling shifting trends over time via topic analysis of text documents”. Proceedings of the 34rd European Modeling and Simulation Symposium EMSS2022, Rom, Italy (2022).

Towards Modelling Namespaces in Graph Databases

Pointner A., Praschl C., Krauss O. “Towards Modelling Namespaces in Graph Databases”. Proceedings of the 34rd European Modeling and Simulation Symposium EMSS2022, Rom, Italy (2022).

Abstract

We present a novel approach to store data with different contexts inside a property graph model. We introduce namespaces, similar to namespaces in XML, and extend nodes and relationships with labels to assign them to a specific context, i.e. namespace. Individual properties of a node or relationship can also be put in a namespace. This work is specifically targeting the utilization in graph databases, with a reference implementation provided via the Neo4j database. In addition to the theoretical approach, an object to graph mapper for the programming language Java is implemented and used to evaluate the approach. As an evaluation example, a university organization is used, which is split into two domains. The experiments show, that information of different domains can be stored within the same model using namespaces. Thus, it is possible to reuse shared information over multiple contexts, which reduces data duplication in the graph database, as otherwise multiple nodes would be required.

Strategies for Semi-Automated Registration of Historic Aerial Photographs Utilizing Street and Roof Segmentations as Durable Landmarks

Zwettler, G., Ono, Y., Stradner, M., & Praschl, C. “Strategies for Semi-Automated Registration of Historic Aerial Photographs Utilizing Street and Roof Segmentations as Durable Landmarks”. Proceedings of the 34th European Modeling and Simulation Symposium EMSS2022, Rom, Italy (2022).

Abstract

Historical and current aerial photographs are only of great value if the geolocation or address of the photographed areas is also available. In Western Europe, especially Austria, Germany and Czech Republic, there is a market for the sale of aerial photographs of one’s own private residential building. Automated geolocation is a feasible way to enable the sales agents to assign the addresses for the sale more quickly. In the course of this research work, a process chain is modeled that allows the assignment of aerial photographs to residential addresses using machine vision. After model-based rectifying the aerial images to compensate for perspective distortions, larger image blocks get assembled using image stitching. The assignment to a 2D reference map, such as satellite imagery via Google Maps, is done by applying a U-Net CNN after extracting durable image features such as roads or buildings. The mapping of aerial imagery to two-dimensional cartography is either automated via registration approaches or based on manually placed corresponding landmarks and homography. Test runs on imagery between the years 1969 and 2020 show that the labor-intensive process of geolocation of aerial imagery can be solved by the proposed process model in a hybrid way.

Distributed Classification - A Scalable Approach to Semi SupervisedMachine Learning

Meindl R., Sandler S., Mayrhuber E., and Krauss O. “Distributed Classification – A Scalable Approach to Semi SupervisedMachine Learning” Proceedings of the 34th European Modeling and Simulation Symposium EMSS2022, Rom, Italy (2022).

Abstract

Fitting real world data into a model for classification, is a challenging task. Modern approaches to classification are often resource intensive and may become bottlenecks. A microservice architecture that allows maintaining a model of real world data, and adding new information as it becomes available is presented in this paper. Updates to the model are handled via different microservices. The architecture and connected workflows are demonstrated in a use case of classifying text data in a taxonomy represented by a directed acyclic graph (DAG). The presented architecture removes the classification bottleneck, as multiple data points can be added independent of each other, and reading access to the model is not restricted. Additional microservices also enable a manual intervention to update the model.

Model Verification in Graph Databases and its Application in Neo4j

Praschl C., Pointner A., Krauss O., Helm E., Schuler A. “Model Verification in Graph Databases and its Application in Neo4j.” Proceedings of the 34th European Modeling and Simulation Symposium EMSS2022, Rom, Italy (2022).

Abstract

This work introduces a concept for rule based model verification using a graph database on the example of Neo4j and its query language Cypher. An approach is provided that allows to define verification rules using a graph query language to detect transformation errors within a given domain model. The approach is presented based on a running example, showing its capability of detecting randomly generated errors in a transformation process. Additionally, the method’s performance is evaluated using multiple subsets of the IMDb movie data with a maximum of 17,000,000 nodes and 41,000,000 relationships. This performance evaluation is carried out in comparison to the Object Constraint Language, showing advantages in the context of highly connected datasets with a high number of nodes. Another benefit is the utilization of a well established graph database as verification tool without any need for re-implementing graph and pattern matching logic.

Long-Term Monitoring of Alpine Plant Diversity in the National Park Hohe Tauern

Kaiser R., Praschl C., Zwettler G. “Long-Term Monitoring of Alpine Plant Diversity in the National Park Hohe Tauern”. 7. Symposium for research in protected areas. Conference. Vol. 2. 2022.

Abstract

The Hohe Tauern National Park has founded an interdisciplinary monitoring and research program for long-term observation of alpine ecosystems. This initiative provides – among other findings – an ongoing digital image archive in the form of strictly standardized (geo-static, colourfast), high-resolution (1 px. ≈ 0,1mm) nadir photos (view vertical to the ground) with high information content and great relevance in terms of documentation. These data, comparable to earth orthophotos, represent the basis for the project at hand. It focuses on developing a software prototype to automatically recognize plants from image data using computer vision and machine learning. The goals are threefold. First, the reliable recognition of individual plant species and their individuals, despite overlap with other plants or vegetation structures, is aimed. Secondly, the variation in nature and thus divergent appearance of a specimen is addressed. Thirdly, it should be possible to detect identical plants within a time series. In addition, this should allow the models to be updated when new data are available.

Anomaly-Based Risk Detection Using Digital News Articles

Pointner, A., Spitzer, EM., Krauss, O., Stöckl, A. (2023). Anomaly-Based Risk Detection Using Digital News Articles. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2022. Lecture Notes in Networks and Systems, vol 542. Springer, Cham.

DOI: https://doi.org/10.1007/978-3-031-16072-1_1

Abstract

Enterprise risk management is a well established methodology used in industry. This area relies heavily on risk owners and their expert opinion. In this work, we present an approach to a semi-automated risk detection for companies using anomaly detection. We present various anomaly detection algorithms and present an approach on how to apply them on multidimensional data sources like news articles and stock data to automatically extract possible risks. To do so, NLP methods, including sentiment analysis, are used to extract numeric values from news articles, which are needed for anomaly analysis. The approach is evaluated by conducting interview questionnaires with domain experts. The results show that the presented approach is a useful tooling that helps risk owners and domain expert to find and detect potential risks for their companies.

Building Process-Oriented Data Science Solutions for Real-World Healthcare

Fernandez-Llatas, Carlos, et al. “Building Process-Oriented Data Science Solutions for Real-World Healthcare.” International Journal of Environmental Research and Public Health 19.14 (2022): 8427.

DOI: https://doi.org/10.3390/ijerph19148427

Abstract

The COVID-19 pandemic has highlighted some of the opportunities, problems and barriers facing the application of Artificial Intelligence to the medical domain. It is becoming increasingly important to determine how Artificial Intelligence will help healthcare providers understand and improve the daily practice of medicine. As a part of the Artificial Intelligence research field, the Process-Oriented Data Science community has been active in the analysis of this situation and in identifying current challenges and available solutions. We have identified a need to integrate the best efforts made by the community to ensure that promised improvements to care processes can be achieved in real healthcare. In this paper, we argue that it is necessary to provide appropriate tools to support medical experts and that frequent, interactive communication between medical experts and data miners is needed to co-create solutions. Process-Oriented Data Science, and specifically concrete techniques such as Process Mining, can offer an easy to manage set of tools for developing understandable and explainable Artificial Intelligence solutions. Process Mining offers tools, methods and a data driven approach that can involve medical experts in the process of co-discovering real world evidence in an interactive way. It is time for Process-Oriented Data scientists to collaborate more closely with healthcare professionals to provide and build useful, understandable solutions that answer practical questions in daily practice. With a shared vision, we should be better prepared to meet the complex challenges that will shape the future of healthcare.

Utilization of Geographic Data for the Creation of Occlusion Models in the Context of Mixed Reality Applications

Praschl C., Thiele E., Krauss O. “Utilization of Geographic Data for the Creation of Occlusion Models in the Context of Mixed Reality Applications”. Extended Reality, 1st ed. Lecture Notes in Computer Science. Volume 13446. (2022).

DOI: https://doi.org/10.1007/978-3-031-15553-6

Abstract

Emergency responder training can benefit from outdoor use of Mixed Reality (MR) devices to make trainings more realistic and allow simulations that would otherwise not be possible due to safety risks or cost-effectiveness. But outdoor use of MR requires knowledge of the topography and objects in the area to enable accurate interaction of the real world trainees experience and the virtual elements that are placed in them. An approach utilizing elevation data and geographic information systems to create effective occlusion models is shown, that can be used in such outdoor training simulations. The initial results show that this approach enables accurate occlusion and placement of virtual objects within an urban environment. This improves immersion and spatial perception for trainees. In the future, improvements of the approach are planned with on the fly updates to outdated information in the occlusion models.

How do Android developers improve non-functional properties of software?

Callan, James, et al. “How do Android developers improve non-functional properties of software?.” Empirical Software Engineering 27.5 (2022): 1-42.

DOI: https://doi.org/10.1007/s10664-022-10137-2

Abstract

Nowadays there is an increased pressure on mobile app developers to take non-functional properties into account. An app that is too slow or uses much bandwidth will decrease user satisfaction, and thus can lead to users simply abandoning the app. Although automated software improvement techniques exist for traditional software, these are not as prevalent in the mobile domain. Moreover, it is yet unknown if the same software changes would be as effective. With that in mind, we mined overall 100 Android repositories to find out how developers improve execution time, memory consumption, bandwidth usage and frame rate of mobile apps. We categorised non-functional property (NFP) improving commits related to performance to see how existing automated software improvement techniques can be improved. Our results show that although NFP improving commits related to performance are rare, such improvements appear throughout the development lifecycle. We found altogether 560 NFP commits out of a total of 74,408 commits analysed. Memory consumption is sacrificed most often when improving execution time or bandwidth usage, although similar types of changes can improve multiple non-functional properties at once. Code deletion is the most frequently utilised strategy except for frame rate, where increase in concurrency is the dominant strategy. We find that automated software improvement techniques for mobile domain can benefit from addition of SQL query improvement, caching and asset manipulation. Moreover, we provide a classifier which can drastically reduce manual effort to analyse NFP improving commits.

A Scalable Microservice Infrastructure for Fleet Data Management

Meindl, Rainer, et al. “A Scalable Microservice Infrastructure for Fleet Data Management.” International Conference on Database and Expert Systems Applications. Springer, Cham, 2022.

DOI: https://doi.org/10.1007/978-3-031-14343-4_37

Abstract

Modern Internet of Things solutions using edge devices produce large amounts of raw data. In order to utilize this data, it needs to be processed, aggregated, and categorized to enable decision making for management and end-users. This data management is a non-trivial task, as the computational load is directly proportional to the amount of data. In order to tackle this issue, we provide an extensible and scalable microservice architecture that can receive, normalize, and filter the raw data and persist it in different levels of aggregation, as well as for time series analysis.

Accurately Predicting User Registration in Highly Unbalanced Real-World Datasets from Online News Portals.

Spitzer, Eva-Maria, Oliver Krauss, and Andreas Stöckl. “Accurately Predicting User Registration in Highly Unbalanced Real-World Datasets from Online News Portals.” International Conference on Database and Expert Systems Applications. Springer, Cham, 2022.

DOI: https://doi.org/10.1007/978-3-031-12423-5_23

Abstract

Getting visitors to register is a crucial factor in marketing for online news portals. Current approaches are rule-based by awarding points for specific actions [3]. Finding efficient rules can be challenging and depends on the specific task. Registration is generally rare compared to regular visitors, leading to highly imbalanced data.

We analyze different supervised learning classification algorithms under consideration of the data imbalance. As case study, we use anonymized real-world data from an Austrian newspaper outlet containing the visitor’s session behavior with around 0.1% registrations over all visits.

We identify an ensemble approach combining the Balanced Random Forest Classifier and the RUSBoost Classifier correctly identifying 76% of registrations over five independent data sets

Towards an Automated System for Reverse Geocoding of Aerial Photographs

Praschl C., Stradner M., Ono Y., Zwettler G. “Towards an Automated System for Reverse Geocoding of Aerial Photographs”. WSCG 2022: proceedings: 30. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 296-301. (2022).

DOI: https://www.doi.org/10.24132/CSRN.3201.37

Abstract

Aerial photographs of buildings are often used as memorabilia sold by trading companies. Such photographs come with an issue regarding the address of the shown buildings, since the recording location of the camera may be known, but shows a spatial distance to the actual subject of the image. In addition to that, also this recording location is often not known in detail but only roughly in the form of the flight route/area. To address this problem, a methodology for reverse geocoding is proposed, allowing to identify the position of buildings that are photographed from aerial vehicles. This is done using a process for extending recording locations and a second process based on the registration of invariant features within aerial shots compared to maps.

Geo-Referenced Occlusion Models for Mixed Reality Applications using the Microsoft HoloLens

Praschl C., Krauss O. “Geo-Referenced Occlusion Models for Mixed Reality Applications using the Microsoft HoloLens.” VISIGRAPP (3: IVAPP). 2022.

DOI: http://dx.doi.org/10.5220/0010775200003124

Abstract

Emergency responders or task forces can benefit from outdoor Mixed Reality (MR) trainings, as they allow more realistic and affordable simulations of real-world emergencies. Utilizing MR devices for outdoor situations requires knowledge of real-world objects in the training area, enabling the realistic immersion of both, the real, as well as the virtual world, based on visual occlusions. Due to spatial limitations of state-of-the-art MR devices recognizing distant real-world items, we present an approach for sharing geo-referenced 3D geometries across multiple devices utilizing the CityJSON format for occlusion purposes in the context of geospatial MR visualization. Our results show that the presented methodology allows accurate conversion of occlusion models to geo-referenced representations based on a quantitative evaluation with an average error according to the vertices’ position from 1.30E-06 to 2.79E-04 (sub-millimeter error) using a normalized sum of squared errors metric. In the future, we plan to also incorporate 3D reconstructions from smartphones and drones to increase the number of supported devices for creating geo-referenced occlusion models.

Open Infrastructure for Standardization of HL7® FHIR® Implementation Guides in Austria.

Egelkraut, Reinhard, et al. “Open Infrastructure for Standardization of HL7® FHIR® Implementation Guides in Austria.” dHealth. 2022.

DOI: https://doi.org/10.3233/shti220372

Abstract

Background: HL7 Austria is a non-profit association dedicated to improving electronic data communication and interoperability in healthcare using the HL7 international standards. Objectives: We aim to provide an open infrastructure to develop, manage, and maintain HL7 FHIR implementation guides. Methods: We utilize state-of-the-art open-source tooling developed by the FHIR community to support continuous integration. Results: The implementation guides can be published as static HTML websites and maintained using GitHub. Conclusion: The solution supports all steps of a standard’s lifecycle, from drafting and reviewing to balloting, publishing, and maintenance.

Segmentation and Multi-Facet Classification of Individual Logs in Wooden Piles

Praschl C. Auserperg-Castell P., Forster-Heinlein B., Zwettler G. (2021). Segmentation and Multi-Facet Classification of Individual Logs in Wooden Piles. In Computer Aided Systems Theory Extended Abstract.

Link: https://eurocast2022.fulp.ulpgc.es/sites/default/files/Eurocast_2022_Extended_Abstract_Book.pdf

Abstract

The inspection of products and assessment of quality is connected with high costs and time effort in many industrial domains. This also applies to the forestry industry. Utilizing state-of-the-art deep learning models allows automizing the analysis of wooden piles in a vision-based manner. In this work a parallel two-step approach is presented for the segmentation and multi-facet classification of individual logs, according to the wood type and quality. The present approach is based on a preliminary log localization step and like this allows determining the quality, volume and also the value of individual logs, respectively the whole wooden pile. Using a YOLOv4 model for wood species classification for douglas firs, pines and larches results in an accuracy of 74.53%, while a quality classification model for spruce logs reaches 86.58%. In addition to that, the trained U-NET segmentation model reaches an accuracy of 93%. In the future, the underlying data set and models will be further improved and integrated to a mobile application for the on site analyzation of wooden piles by foresters.

Process Mining for Healthcare: Characteristics and Challenges

Jorge Munoz-Gama, Niels Martin, Carlos Fernandez-Llatas, Owen A. Johnson, Marcos Sepúlveda, Emmanuel Helm, Victor Galvez-Yanjari, Eric Rojas, Antonio Martinez-Millana, Davide Aloini, Ilaria Angela Amantea, Robert Andrews, Michael Arias, Iris Beerepoot, Elisabetta Benevento, Andrea Burattin, Daniel Capurro, Josep Carmona, Marco Comuzzi, Benjamin Dalmas, Rene de la Fuente, Chiara Di Francescomarino, Claudio Di Ciccio, Roberto Gatta, Chiara Ghidini, Fernanda Gonzalez-Lopez, Gema Ibanez-Sanchez, Hilda B. Klasky, Angelina Prima Kurniati, Xixi Lu, Felix Mannhardt, Ronny Mans, Mar Marcos, Renata Medeiros de Carvalho, Marco Pegoraro, Simon K. Poon, Luise Pufahl, Hajo A. Reijers, Simon Remy, Stefanie Rinderle-Ma, Lucia Sacchi, Fernando Seoane, Minseok Song, Alessandro Stefanini, Emilio Sulis, Arthur H.M. ter Hofstede, Pieter J. Toussaint, Vicente Traver, Zoe Valero-Ramon, Inge van de Weerd, Wil M.P. van der Aalst, Rob Vanwersch, Mathias Weske, Moe Thandar Wynn, Francesca Zerbato. Process Mining for Healthcare: Characteristics and Challenges, Journal of Biomedical Informatics. 2022.

DOI: https://doi.org/10.1016/j.jbi.2022.103994

Abstract

Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.

Geo-Referenced Occlusion Models for Mixed Reality Applications Using the Microsoft HoloLens

C. Praschl, O. Krauss. Geo-Referenced Occlusion Models for Mixed Reality Applications Using the Microsoft HoloLens. Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Volume 3: IVAPP. 2022.

DOI: https://doi.org/10.5220/0010775200003124

Abstract

Three-Step Approach for Localization, Instance Segmentation and Multi-Facet Classification of Individual Logs in Wooden Piles

C. Praschl, G. Zwettler. Three-Step Approach for Localization, Instance Segmentation and Multi-Facet Classification of Individual Logs in Wooden Piles. Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods. 2022.

DOI: https://doi.org/10.5220/0010892100003122

Abstract

The inspection of products and the assessment of quality is connected with high costs and time effort in many industrial domains. This also applies to the forestry industry. Utilizing state-of-the-art deep learning models allows the analysis automation of wooden piles in a vision-based manner. In this work a three-step approach is presented for the localization, segmentation and multi-facet classification of individual logs based on a client/server architecture allowing to determine the quality, volume and like this the value of a wooden pile based on a smartphone application. Using multiple YOLOv4 and U-NET models leads to a client-side log localization accuracy of 82.9% with low storage requirements of 23 MB and a server-side log detection accuracy of 94.1%, together with a log type classification accuracy of 95% and 96% according to the quality assessment of spruce logs. In addition, the trained segmentation model reaches an accuracy of 89%.

FHIR2BPMN: Delivering Actionable Knowledge by Transforming Between Clinical Pathways and Executable Models

Helm, Emmanuel, et al. “FHIR2BPMN: Delivering Actionable Knowledge by Transforming Between Clinical Pathways and Executable Models.” Healthcare of the Future 2022. IOS Press, 2022. 9-14.

DOI: https://doi.org/10.3233/SHTI220311

Abstract

Healthcare processes have many particularities captured and described within standards for medical information exchange such as HL7 FHIR. BPMN is a widely used standard to create readily understandable processes models. We show an approach to integrate both these standards via an automated transformation mechanism. This will allow us to use the various tools available for BPMN to visualize and automate processes in the healthcare domain. In the future we plan to extend this approach to enable mining and analyzing executed processes.

2021

Imaging framework: An interoperable and extendable connector for image-related Java frameworks

C. Praschl, A. Pointner, D. Baumgartner, G. Zwettler. Imaging framework: An interoperable and extendable connector for image-related Java frameworks. SoftwareX, Volume 16. 2021.

DOI: https://doi.org/10.1016/j.softx.2021.100863

Abstract

The number of computer vision and image processing tasks has increased during the last years. Although Python is most of the time the first choice in this area, there are situations, where the utilization of another programming language such as Java should be preferred. For this reason, multiple Java based frameworks as e.g. OpenIMAJ, ND4J or multiple OpenCV wrappers are available. Unfortunately, these frameworks are not interoperable at all. In this work, the open-source Imaging Framework is introduced to solve exactly this problem. The project features a concept for combining multiple frameworks and provides an interoperable and extendable foundation to 9 image-related projects with 10 different image representations in Java.

How can Interoperability Support Process Mining in Healthcare?

Helm, Emmanuel, and Oliver Krauss. “How can Interoperability Support Process Mining in Healthcare?.” PODS4H.

Abstract

A discussion of the relationship between the concept of healthcare information systems interoperability and process-oriented data analysis. The goal is to show that some of the current challenges of process mining in healthcare are also interoperability problems. By participating in solving these problems we can also improve our data sources.

Mining API Interactions to Analyze Software Revisions for the Evolution of Energy Consumption

A. Schuler and G. Kotsis, “Mining API Interactions to Analyze Software Revisions for the Evolution of Energy Consumption,” in 2021 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR),, 2021 pp. 312-316.

DOI: https://doi.ieeecomputersociety.org/10.1109/MSR52588.2021.00043

Abstract

With the widespread use and adoption of mobile platforms like Android a new software quality concern has emerged – energy consumption. However, developing energy-efficient software and applications requires knowledge and like-wise proper tooling to support mobile developers. To this aim, we present an approach to examine the energy evolution of software revisions based on their API interactions. The approach stems from the assumption that the utilization of an API has direct implications on the energy being consumed during runtime. Based on an empirical evaluation, we show initial results that API interactions serve as a flexible, lightweight, and effective way to compare software revisions regarding their energy evolution. Given our initial results we envision that in future using our approach mobile developers will be able to gain insights on the energy implications of changes in source code in the course of the software development life-cycle.

EpiMon: Vision-Based Early Warning System for Monitoring Uprising Epileptic Seizures During Night

Zwettler G., Reichhard A. Stradner M., Praschl C., Helm E.(2021). In Proceedings of 33rd European Modeling & Simulation Symposium.

DOI: https://doi.org/10.46354/i3m.2021.iwish.004

Abstract

At a prevalence of almost 1%, potential epileptic seizures manifest a significant health risk for many juvenile patients. Thus, monitoring is essential to set early counteractive measurements to prevent from damage. The sensor-based monitoring systems mainly address epileptic seizures indicated by a change in the muscle tonus but cannot be utilized for patients that show Prévost’s-sign only. To monitor initiating Prévost’s-sign with opened-eyes as critical visual feature, the applicability of deep-learning eye detection systems on night vision images is evaluated in this paper as basis for modelling and classifying the eye state (closed, opened, not visible). A holistic research prototype is presented as proof of concept, showing the applicability of state-of-the-art face detection on night vision images as well as multi-variate feature analysis on Graph segmentation pre-fragmentation, applicable to detect the state of the eye in a robust way. Results show a single frame accuracy in face/eye detection of 73.91% and 94.44% for classification of the opened eyes as indication of a potentially initiating epileptic seizure. The monitoring system is based on a Raspberry computation unit with two ELP night vision cameras attached and a smart phone app for user-interaction and configuration besides on-demand visual monitoring. Future work will show that the single frame detection rate is sufficient for building up a rule-based monitoring state machine at user predefined sensitivity and specificity by analysing the visual content as time-series rather than single images.

Practicable Paradigms for Semi-Automated Expert-User Post-Processing of Deep-Learning Segmentations in 3D Radiology

Jany J., Zwettler G. (2021). In Proceedings of 33rd European Modeling & Simulation Symposium.

DOI: https://doi.org/10.46354/i3m.2021.iwish.007

Abstract

With recent improvements in deep-learning architectures and availability of GPU hardware, state of the art deep learning (DL) has already manifested as powerful image processing technology in the clinical routine to provide segmentation results of high accuracy. As a drawback, it’s black-box nature does not naturally feature inspection and post-processing by medical experts. We present a Graph segmentation (GS) approach that derives it’s fitness function from arbitrary DL results in a generic way. To allow for efficient and effective post-processing by the medical experts, various interaction paradigms are presented and evaluated in this paper. The trade-off of GS compared to the initial DL results is marginal (delta JI= 0.196%), yet potential DL segmentation errors can be corrected in a reliable way. The intuitive approach shows a high level of both, inter and intra user reproducibility. Change propagation of corrected slices keeps the demand for user-interaction to a minimum when successfully correction potential weaknesses in the DL segmentations. Thereby, the formerly error-prone slice mini-batches get corrected in an automated way with the JI being significantly increased.

Multi-Resolution Localization of Individual Logs in Wooden Piles Utilizing YOLO with Tiling on Client/Server Architectures

Praschl C. Auserperg-Castell P., Forster-Heinlein B., Zwettler G. (2021). In Proceedings of 33rd European Modeling & Simulation Symposium.

DOI: https://doi.org/10.46354/i3m.2021.emss.042

Abstract

In industrial domains with time and cost intensive manual or semi-automated inspection the demand for automation is high. Utilizing state of the art deep learning models for localization in vision-based domains such as wood log analysis, the precision can be increased while reducing the demand for manual inspection. In this paper a YOLO network is trained on wood log images to allow for detection of single wood piles in images with hundreds and thousands instances. Due to the high variability in scale and large amount of wood logs within the images, common YOLO architectures are not applicable. Thus, tiling is necessitated to implicitly form a multi-resolution image pyramid. Due to lack in training data, besides common data augmentation modelling of different seasonal and weather conditions is applied. The wood log detection process can be run on a client/server architecture to allow for both, preview and refined results. Evaluation on real-world data sets shows an log detection accuracy of 82,9% utilizing a tiny YOLO model and 94,1% with a fully connected YOLO model, respectively.

Secure Peer Review and Feedback on Medical Findings via IHE XDS

Helm E., Schwebach J., Pointner A., Lin A., Rothensteiner V., Keimel D. and Schuler A. (2021). In Proceedings of dHealth 2021 – Health Informatics Meets Digital Health.

Abstract

There is a lack of secure official communication channels for peer review and peer feedback on medical findings. Objectives: We aimed to utilize the existing Austrian eHealth infrastructure to enable review and feedback processes. Methods: We extended the IHE XDW workflow document to enable the exchange of text messages (i.e., comments on documents or images) over an XDS infrastructure. Results: The workflow enabled the exchange of comments on specific sections of CDA documents or radiological images and was verified in an XDS test environment. Conclusion: The presented solution is a proof of concept that could lead to the specification of a new IHE workflow definition.

Process Mining on FHIR - An Open Standards-Based Process Analytics Approach for Healthcare

Helm E., Krauss O., Lin A., Pointner A., Schuler A. and Küng J. (2021). Process Mining on FHIR – An Open Standards-Based Process Analytics Approach for Healthcare. In Process Mining Workshops.

Abstract

Process mining has become its own research discipline over the last years, providing ways to analyze business processes based on event logs. In healthcare, the characteristics of organizational and treatment processes, especially regarding heterogeneous data sources, make it hard to apply process mining techniques. This work presents an approach to utilize established standards for accessing the audit trails of healthcare information systems and provides automated mapping to an event log format suitable for process mining. It also presents a way to simulate healthcare processes and uses it to validate the approach.

Further information can be found here

Genetic Improvement of Data for Maths Functions*

Langdon W. and Krauss O. (2021). Genetic Improvement of Data for Maths Functions*. In Proceedings of the Genetic and Evolutionary Computation Conference Companion.

Abstract

Genetic Improvement (GI) can be used to give better quality software and to create new functionality.
We show that GI can evolve the PowerPC open source GNU C runtime library square root function into cube root, binary logarithm log2 and reciprocal square root.
The GI cbrt is competitive in run-time performance and our inverted square root x**-0.5 is far more accurate than the approximation used in the Quake video game.
We use CMA-ES to adapt constants in a Newton-Raphson table, originally from glibc’s sqrt, for other double precision mathematics functions.
Such automatically customised math libraries might be used for mobile or low resource, IoT, mote, smart dust, bespoke cyber-physical systems.
Evolutionary Computing (EC) can be used to not only adapt source code but also data, such as numerical constants, and could enable a new way to conduct software data maintenance.
This is an exciting opportunity for the GECCO and optimisation communities.

Further information can be found here

Line Clustering and Contour Extraction in the Context of 2D Building Plans

Pointner A., Praschl C., Krauss O., Schuler A., Helm E., and Zwettler G. (2021). Line Clustering and Contour Extraction in the Context of 2D Building Plans. In Proceedings of 29. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision.

DOI: https://doi.org/10.24132/CSRN.2021.3101.2

Abstract

For the purpose of analyzing a building according to its accessibility or structural resilience, printed 2D floor plans are not sufficient because of the missing link to semantic information.
This paper tackles this issue and introduces a concept for clustering classified lines of a floor plan and for creating semantically enriched contour elements based on different image processing,
computer vision and machine learning algorithms. Based on a general line clustering approach, we introduce type specific methods for walls, windows, doors and stairs.
The resulting clusters are in turn used for a contour creation, which uses minimal rotated rectangles. Those rectangles are transformed to polygons that are refined using post processing steps.The approach is evaluated via positive testing using a pixel-based comparison of the process’s result. For this, automatically generated as well as real world building plans are used. The final evaluation shows, that the concept reaches a confidence of >90% for door, stair and windows and only around 10% for stairs with the run-time linearly scaling with the size of the input.

Process Mining in Healthcare

Fernandez-Llatas, C., Munoz-Gama, J., Martin, N., Johnson, O., Sepulveda, M., & Helm, E. (2021). Process Mining in Healthcare. In Interactive Process Mining in Healthcare (pp. 41-52). Springer, Cham.

Abstract

Since medical processes are hard to be designed by consensus of experts, the use of data available for creating medical processes is a recurrent idea in literature. Data-driven paradigms are named to be a feasible solution in this field that can support medical experts in their daily decisions. Behind this paradigm, there are frameworks specifically designed for dealing with process-oriented problems. This is the case of process mining.

Transfer Learning and Hyperparameter Optimization for Instance Segmentation with RGB-D Images in Reflective Elevator Environments

Reithmeier, L.; Krauss, O. and Zwettler, G.; (2021). Transfer Learning and Hyperparameter Optimization forInstance Segmentation with RGB-D Images in Reflective Elevator Environments. In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications – Volume 5: VISAPP, ISBN 978-989-758-488-6.

DOI: https://doi.org/10.24132/CSRN.2021.3101.30

Abstract

Elevators, a vital means for urban transportation, are generally lacking proper emergency call systems besides an emergency button. In the case of unconscious or otherwise incapacitated passengers this can lead to lethal situations. A camera-based surveillance system with AI-based alerts utilizing an elevator state machine can help passengers unable to initiate an emergency call. In this research work, the applicability of RGB-D images as input for instance segmentation in the highly reflective environment of an elevator cabin is evaluated. For object segmentation, a Region-based Convolution Neural Network (R-CNN) deep learning model is adapted to use depth input data besides RGB by applying transfer learning, hyperparameter optimization and re-training on a newly prepared elevator image dataset. Evaluations prove that with the chosen strategy, the accuracy of R-CNN instance segmentation is applicable on RGB-D data, thereby resolving lack of image quality in the noise affected and reflective elevator cabins. The mean average precision (mAP) of 0.753 is increased to 0.768 after the incorporation of additional depth data and with additional FuseNet-FPN backbone on RGB-D the mAP is further increased to 0.794. With the proposed instance segmentation model, reliable elevator surveillance becomes feasible as first prototypes and on-road tests proof.

Three-step Alignment Approach for Fitting a Normalized Mask of a Person Rotating in A-Pose or T-Pose Essential for 3D Reconstruction based on 2D Images and CGI Derived Reference Target Pose

Zwettler, G.; Praschl, C.; Baumgartner, D.; Zucali, T.; Turk, D.; Hanreich, M. and Schuler, A. (2021). Three-step Alignment Approach for Fitting a Normalized Mask of a Person Rotating in A-Pose or T-Pose Essential for 3D Reconstruction based on 2D Images and CGI Derived Reference Target Pose.In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications – Volume 5: VISAPP, ISBN 978-989-758-488-6, pages 281-292. DOI: 10.5220/0010194102810292

Abstract

The 3D silhouette reconstruction of a human body rotating in front of a monocular camera system is a very challenging task due to elastic deformation and positional mismatch from body motion. Nevertheless, knowledge of the 3D body shape is a key information for precise determination of one’s clothing sizes, e.g. for precise shopping to reduce the number of return shipments in online retail. In this paper a novel three step alignment process is presented, utilizing As-Rigid-As-Possible (ARAP) transformations to normalize the body joint skeleton derived from OpenPose with a CGI rendered reference model in A- or T-pose. With further distance-map accelerated registration steps, positional mismatches and inaccuracies from the OpenPose joint estimation are compensated thus allowing for 3D silhouette reconstruction of a moving and elastic object without the need for sophisticated statistical shape models. Tests on both, artificial and real-world data, generally proof the practicability of th is approach with all three alignment/registration steps essential and adequate for 3D silhouette reconstruction data normalization.

2020

Automatic Detection of Objects Blocking Elevator Doors using Computer Vision

Baumgartner D., Jordens I., Wilfing D., Krauss O., Zwettler G. (2020). Automatic Detection of Objects Blocking Elevator Doors using Computer Vision. In Proceedings of the 23rd International Congress on Vertical Transportation Technologies.

Abstract

In this paper we present a new approach applying computer vision methods to image data acquired with depth perception cameras to map the interior of the elevator, detect the position and the state of the door and to detect objects in the door area. The depth data is used to determine the elevator cabin as safety cube, i.e. the position of the door, layout of the elevator and so on, while color data further enhances the detection of new objects. The approach can detect the state of the elevator door as either opened or closed, while no object is blocking the view to the door, as well as successfully identify objects blocking an open door. This elevator monitoring proves to be relevant for determination of the elevator state, safety as well aspects of predictive maintenance.

Towards the Use of Standardized Terms in Clinical Case Studies for Process Mining in Healthcare

Towards Knowledge-guided Genetic Improvement

Proceedings of the 8th International Workshop on Genetic Improvement (Details)
Krauss O., Mössenböck H., Affenzeller M.

Evolving sqrt into 1/x via Software Data Maintenance

GECCO ’20: Proceedings of the Genetic and Evolutionary Computation Conference Companion (Details)
Langdon W., Krauss O.

Automatically Evolving Lookup tables for Function Approximation

Genetic Programming. EuroGP 2020. Lecture Notes in Computer Science, vol 12101. Springer, Cham (Details)
Krauss O., Langdon W.B.

Strategies for Training Deep Learning Models in Medical Domains with Small Reference Datasets

Gerald Zwettler, David Holmes and Werner Backfrieder. “Strategies for Training Deep Learning Models in Medical Domains with Small Reference Datasets”. WSCG ’20.

Abstract

With the continuous progress of Deep Learning (DL) powerful tools are now available for sophisticated segmentation tasks. Nevertheless, the generally very high demand for training data and precise reference segmentations in the medical domain often cannot be met when dealing with small and individual studies or acquisition protocols. As common strategies, reinforcement learning or transfer learning are applicable, but coherent with immense effort due to domain-specific adaptation. In this work, we evaluate the applicability of a U-grid cascade for training on a very small set of abdominal MRI datasets of the parenchyma and discuss strategies to compensate for the lack of training data. Although model accuracy is rather low when training on 13 MRI bands with achievable JI=89.41, the results are still good enough for annual post-processing using a graph-cut (GC) approach with moderate user interaction requirements. In this way, DL models are retrained as additional test data sets become available to subsequently improve classification accuracy. With only 2 additional GC post-processing datasets, the accuracy after model retraining is JI= 89.87. Furthermore, the applicability of Generative Adversarial Networks (GAN) in the medical field is evaluated, discussing to synthesize axial CT slices together with perfect ground truth reference segmentations. It is shown for abdominal CT slices of the parenchyma that in the absence of training data, synthesized slices that can be derived in arbitrary numbers can significantly improve the DL training process when only an insufficient amount of data is available. While training on 2,200 real-world images only leads to an accuracy of JI=88.75, enrichment with 2,200 additional images synthesized from a GAN trained on 5,000 datasets leads to an increase up to JI=92.02. Even when the DL model is trained exclusively on 4,400 computer-generated images, the classification accuracy on real-world data is remarkable with JI=90.81.

Pre- and Post-processing Strategies for Generic Slice-wise Segmentation of Tomographic 3D datasets Utilizing U-Net Deep Learning Models Trained for Specific Diagnostic Domains

G. Zwettler, D. Holmes III, W. Backfrieder – Pre- and Post-processing Strategies for Generic Slice-wise Segmentation of Tomographic 3D datasets Utilizing U-Net Deep Learning Models Trained for Specific Diagnostic Domains – Proceedings of the VISAPP 2020, Valetta, Malta, 2020, pp. 66-78

Abstract

An automated and generally applicable method for segmentation is still in the focus of medical image processing research. For several years, artificial intelligence methods have shown promising results, especially with widely available scalable deep learning libraries. In this work, a five-layer hybrid U-network is developed for slice-wise segmentation of liver datasets. The training data is obtained from the Medical Segmentation Decathlon database, which contains 131 fully segmented volumes. A slice-based segmentation model is implemented using Deep Learning algorithms with adjustments for variable parenchyma shape along the stacking direction and similarities between adjacent slices. Both are transformed for coronal and sagittal views. The implementation is done on a GPU rack using TensorFlow and Keras. Standardized volume and surface metrics are used for a quantitative measure of segmentation accuracy. The results DSC=97.59, JI=95.29 and NSD=99.37 show correct segmentation comparable to 3D U-meshes and other state of the art U-meshes. The development of a 2D slice oriented segmentation we justified by the advantages of short training times and lower complexity and also massively reduces memory consumption. This work manifests the high potential of AI methods for general application in medicine. Segmentation as a fully or semi-automatic tool under the supervision of the expert user.

Hybrid Approach for Orientation-Estimation of Rotating Humans in Video Frames Acquired by Stationary Monocular Camera

Baumgartner,D., Praschl,C., Zucali,T., Zwettler,G.: 1. Hybrid Approach for Orientation-Estimation of Rotating Humans in Video Frames Acquired by Stationary Monocular Camera

Abstract

Accurate human orientation estimation with respect to the POSE of a monocular camera system is a challenging task due to general aspects of camera calibration and the deformability of a moving human body. Therefore, novel deep learning approaches for precise object position determination in robotics are difficult to adapt for human body analysis. In this work, we present a hybrid approach for accurately estimating a human body relative to a camera system, significantly improving the results derived from poseNet by applying optical flow analysis in a frame-to-frame comparison. The human body, which rotates in the T-position in situ, is thereby center-aligned, with object tracking methods applied to compensate for translations of the body motion. After 2D skeletal extraction, optical flow is calculated for an ROI region aligned relative to the vertical skeletal junction representing the spine and compared frame by frame. To evaluate the suitability of clothing as a basis for good features, local pixel homogeneity is considered to constrain optical flow to heterogeneous regions with distinguishing features such as imprint patterns, buttons, or buckles in addition to local illumination change. Based on the mean optical flow with rough approximation of the axial body shape as an ellipse, accuracy between 0.1° and 2.0° is achieved for orientation estimation on a frame-to-frame comparison evaluated and validated on both CGI renderings and real videos of people wearing clothes with different features.

Enabling outdoor MR capabilities for head mounted displays: a case study

International Journal of Simulation and Process Modelling

C. Praschl, O. Krauss, G. Zwettler

Abstract

This research covers generic approaches to determine the outdoor position and orientation of an augmented reality device due to the lack of outdoor suitability of depth or ambient sensing based devices currently available in the market. Orientation is primarily determined using an Attitude Heading Reference System (AHRS) for rough estimation. Based on a connected/integrated video camera, accuracy is improved for minor changes in orientation by using registration to evaluate orientation differences between two video frames, compensating for gyroscope drift errors. Position determination is performed using GPS with a real-time kinematic beacon system with rover and base station to achieve improved accuracy. The results show that based on the sensor application, AR hardware considered for indoor use can be retrofitted to work properly outdoors, at long distances, and even in moving vehicles. This will facilitate the future implementation of applications in various fields.

Integrating HeuristicLab with Compilers and Interpreters for Non-Functional Code Optimization

Daniel Dorfmeister, and Oliver Krauss. 2020. “Integrating HeuristicLab with Compilers and Interpreters for Non-Functional Code Optimization.” In Proceedings of the Genetic and Evolutionary Computation Conference Companion – GECCO ’20. Cancun, Mexico: ACM Press. (Details).

Adopting Standard Clinical Descriptors for Process Mining Case Studies in Healthcare

Emmanuel Helm, Anna M. Lin, David Baumgartner, Alvin C. Lin und Josef Küng. 2020. “Adopting Standard Clinical Descriptors for Process Mining Case Studies in Healthcare”.

Abstract

Process mining can provide greater insight into medical treatment processes and organizational processes in healthcare. A review of case studies in the literature identified several different common aspects for comparison, which include methods, algorithms or techniques, medical domains, and healthcare specialties. However, from a medical perspective, clinical terms are not used in a consistent manner and do not follow a standardized clinical coding scheme. In addition, the characteristics of event log data are not always described. In this paper, we identified 38 clinically relevant case studies on process mining in healthcare published between 2016 and 2018 that described the tools, algorithms, and techniques used, as well as details about event log data. We then mapped the clinical aspects of the patient encounter environment, clinical specialty, and medical diagnoses using the standard SNOMED CT and ICD-10 clinical coding schemes. The possible results of adopting a standard approach for describing event log data and classifying medical terminology using standard clinical coding schemes are discussed.

2019

The State and Future of Genetic Improvement

IGSOFT Softw. Eng. Notes 44, 3 (July 2019) (Details)
William B. Langdon, Westley Weimer, Christopher Timperley, Oliver Krauss, Zhen Yu Ding, Yiwei Lyu, Nicolas Chausseau, Eric Schulte, Shin Hwei Tan, Kevin Leach, Yu Huang, and Gabin An

arXiv preprint arXiv:1907.03773

Pre- and Post-processing Strategies for Generic Slice-wise Segmentation of Tomographic 3D datasets Utilizing U-Net Deep Learning Models Trained for Specific Diagnostic Domains

Proceedings of the VISAPP 2020, Valetta, Malta, 2020, pp. 12
G. Zwettler, D. Holmes III, W. Backfrieder

Adopting Standard Clinical Descriptors for Process Mining Case Studies in Healthcare

Process-Oriented Data Science for Healthcare (Details)
Emmanuel Helm, David Baumgartner, Anna M. Lin, Alvin Lin, Josef Küng

Mining patterns from genetic improvement experiments

Proceedings of the 6th International Workshop on Genetic Improvement
Oliver Krauss, Hanspeter Mössenböck, Michael Affenzeller

Automated Verification of Structured Questionnaires Using HL7® FHIR®

ICT for Health Science Research
A. Lin, O. Krauss, E. Helm

Process Pruner: A Tool for Sequence-Based Event Log Preprocessing

Process Mining Conference 2019 – 1st International Conference on Process Mining, June 24-26, 2019, Aachen, Germany
Baumgartner D., Haghofer A., Limberger M., Helm E.

Analysis of Heart Rate Variability (HRV) Feature Robustness for Measuring Technostress

Information Systems and Neuroscience, p. 221 – 228, Springer Verlag
Baumgartner D., Fischer T. Riedl R., Dreiseitl S.

2018

Teaching Better Requirements Engineering Using LEGO® Serious Play™

Proc. 9th Intl. Conf. on Society and Information Technologies (ICSIT 2018), Orlando, Vereinigte Staaten von Amerika, 2018, pp. 126-131
Mayr, H.

Towards a Framework for Detecting Energy Drain in Mobile Applications – An Architecture Overview

Proceeding ISSTA ’18 Companion Proceedings for the ISSTA/ECOOP 2018 Workshops Pages 144-149 (Details)
Schuler, A. and Anderst-Kotsis G. – MANA

Process Mining and Conformance Checking of Long Running Processes in the Context of Melanoma Surveillance

International journal of environmental research and public health (Details)
Rinner C., Helm E., Dunkl R., Kittler H., Rinderle-Ma S.

Dynamic fitness functions for genetic improvement in compilers and interpreters

GECCO ’18: Proceedings of the Genetic and Evolutionary Computation Conference Companion (Details)
Krauss, O. Mössenböck, H. Affenzeller, M. – GCE

An Application of Process Mining in the Context of Melanoma Surveillance using Time Boxing

International Conference on Business Process Management (Details)
Rinner C., Helm E., Dunkl R., Kittler H., Rinderle-Ma S.

Model-Based Image Processing Approaches For Automated Person Identification And Authentification In Online Banking

Proceedings of the 30th European Modeling and Simulation Symposium EMSS2018, Budapest, Ungarn, 2018
A. Pointner, O. Krauss, G. Freilinger, D. Strieder, G. Zwettler – GUIDE

Towards Retooling The Microsoft HoloLens As Outdoor AR And MR Device

Proceedings of the 30th European Modeling and Simulation Symposium EMSS2018, Budapest, Ungarn, 2018
C. Praschl, O. Krauss, G. Zwettler – Drive for Knowledge

Towards Semantic Interoperability in Health Data Management Facilitating Process Mining

International Journal of Privacy and Health Information Management (IJPHIM)
Traxler B., Helm E., Krauss O., Schuler A., Kueng J.

A Model for Implementing an Interoperable Electronic Consent Form for Medical Treatment Using HL7 FHIR

European Journal of Biomedical Informatics (Details)
Lackerbauer A., Lin A., Krauss O., Hearn J., Helm E.

Cross-Enterprise Communication and Data Exchange in Radiology in Austria: Technology and Use Cases

Studies in health technology and informatics
Helm E., Schuler A., Mayr H.

2017

An active surface approach for segmentation of bone fragments in trauma surgery planning

Proceedings of the International Workshop on Innovative Simulation for Health Care (IWISH), Barcelona, Spanien, 2017, pp. 26-31
W. Backfrieder, B. Kerschbaumer, G. Zwettler

ADAPTED ICP ALGORITHM FOR SURFACE BASED REGISTRATION IN IMAGE GUIDED SURGERY

Proceedings of the International Workshop on Innovative Simulation for Healthcare IWISH 2017, Barcelona, Spanien, 2017
W. Backfrieder, G. Zwettler, B. Kerschbaumer

Genetic improvement in code interpreters and compilers

Akkordeon InhaltaSPLASH / OOPSLA 2017 (Details)
O. Krauss

Audit trails in OpenSLEX: paving the road for process mining in healthcare

ITBAM 2017, 8th International Conference on Information Technology in Bio-and Medical Informatics, Lyon, France (Details)
González López De Murillas E., Helm E., Reijers HA., Küng J., Bursa M., Holzinger A., Elena Renda M., Khuri S.

Application of search-based software engineering methodologies for test suite optimization and evolution in mission critical mobile application development

Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering (Details)
A. Schuler

Towards standards based health data extraction facilitating process mining

6th International Workshop on Innovative Simulation for Health Care (IWISH 2017) (Details)
E. Helm, B. Franz, A. Schuler, O. Krauss, J. Küng

Challenges and Approaches to Make Multidisciplinary Team Meetings Interoperable – The KIMBo Project.

Studies in Health Technology and Informatics, 2017 – 236 (Details)
Krauss O, Holzer K, Schuler A, Egelkraut R, Franz B. – KIMBO

2016

Process Mining: Towards Comparability of Healthcare Processes.

Information Technology in Bio- and Medical Informatics, Porto, Portugal, 2016 (Details)
E. Helm, J. Küng

Elevator Simulation for Testing Advertisement Scheduling Systems

IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference, Xi’an, Xi’an, China, 2016 (Details)
D. Wilfing, O. Krauss, A. Schuler – ARISE

Heuristics for Job Shop Scheduling with Volatile Machine Constraints

IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference, Xi’an, Xi’an, China, 2016 (Details)
O. Krauss, D. Wilfing, A. Schuler – ARISE

Multidisciplinary Team Meetings-A Literature Based Process Analysis

Information Technology in Bio- and Medical Informatics, Porto, Portugal, 2016 (Details)
O. Krauss, M. Angermaier, E. Helm – KIMBO

2015

Automated On-Demand Generation Of Patient Summary Documents

International Journal of Electronics and Telecommunications, Vol. 61, No. 2, 2015, pp. 151-157
O. Krauss, B. Franz, A. Schuler

Quantifizierung leichter mentaler Erregung durch korrelative Veränderungen von HK- und metabolischen Parametern anhand einer 3-D Gefahrensimulation

NIEREN-UND HOCHDRUCKKRANKHEITEN, Vol. 44, No. 10, 2015, pp. 9
S. Porta, G. Zwettler, W. Kurschl, C. Dinu, G. Juttla, K. Pichlkastner, H. Gell, B. Kaiser, K. Kisters

Topological Fragmentation of Medical 3D Surface Mesh Models for Multi- Hierarchy Anatomical Classification

International Journal of Electronics and Telecommunications, Vol. 60, No. 6, 2015, pp. 1-8
G. Zwettler, W. Backfrieder

Rotated Principal Components for Fuzzy Segmentation Szintigraphic Time Series in Individual Dose Planning

Proceedings of the 2015 I-WISH, The International Workshop on Innovative Simulation for Healthcare , Bergeggi, Italien, 2015, pp. 6
W. Backfrieder, G. Zwettler

Evolution Strategy Classification Utilizing Meta Features and Domainspecific Statistical A Priori Models for Fully-automated and Entire Segmentation of Medical Datasets in 3D Radiology

Proceedings of the IEEE International conference on Computing and Communications Technologies (ICCCT’15), Chennai, Indien, 2015, pp. 1-7
G. Zwettler, W. Backfrieder

Prefetching of Medical Imaging Data Across XDS Affinity Domains

eHealth2015 – Health Informatics Meets eHealth, Wien, Österreich, 2015 (Details)
E. Helm, A. Schuler, O. Krauss, B. Franz

Applying FHIR in an Integrated Health Monitoring System

European Journal for Biomedical Informatics, Vol. 11, No. 2, 2015 (Details)
B. Franz, A. Schuler, O. Krauss

A Comparison of Data Traffic in Standardized Personal Health Monitoring Solutions

International Journal of Electronics and Telecommunications, Vol. 61, No. 2, 2015 (Details)
A. Schuler, B. Franz, O. Krauss

From IHE Audit Trails to XES Event Logs Facilitating Process Mining

MIE, Digital Healthcare Empowering Europeans, Madrid, Spanien, 2015, pp. 40-44 (Details)
F. Paster, E. Helm

First Steps Towards Process Mining in Distributed Health Information Systems

International Journal of Electronics and Telecommunications, Vol. 61, No. 2, 2015, pp. 137-142 (Details)
E. Helm, F. Paster

2014

AUTOMATED DOMAIN-SPECIFIC FEATURE SELECTION FOR CLASSIFICATION-BASED SEGMENTATION OF TOMOGRAPHIC MEDICAL IMAGE DATA

Proceedings of the 3rd International Workshop on Innovative Simulation for Healthcare IWISH 2014, Bordeaux, Frankreich, 2014, pp. 26-35
G. Zwettler, W. Backfrieder

Robust surface based registration in an open framework for image guided surgery

Proceedings of the 3rd International Workshop on Innovative Simulation for Healthcare IWISH 2014, Bordeaux, France, 2014, pp. 36-41
W. Backfrieder, G. Zwettler

Simulation of Tomographic Medical Image Data for Training of Generic Segmentation Models Utilizing Multivariate Feature Classification

California, USA, Vereinigte Staaten von Amerika, 2014, pp. 9
G. Zwettler, W. Backfrieder

Adaptable Topological Model for Segmentation of Medical 3D Data Utilizing Multivariate Heuristic Feature Classification

Tagungsband des 8. Forschungsforum der österreichischen Fachhochschulen, Kufstein, Österreich, 2014, pp. 296-300
G. Zwettler, W. Backfrieder

Applicability of a Standardized Modular Image Processing Model for Analysis of Tomographic 3D Patient Data in Clinical Research and Radiographer Training

Tagungsband des 8. Forschungsforum der österreichischen Fachhochschulen, Kufstein, Österreich, 2014, pp. 482-483
G. Zwettler, W. Backfrieder

General Model-Based Segmentation Strategy for Holistic Analysis of Tomographic Medical Image Data in 3D Radiology

Phd Thesis, University Vienna, Austria, 2014, pp. 1-273
G. Zwettler

Regelbasierte Generierung von Benutzerschnittstellen für Mobile Anwendungsplattformen

Gesundheitswesen im Wandel – nationale und internationale Perspektiven (Editors: Erwin Gollner, Magdalena Thaller) – Leykam, 2014, pp. 30-35 (Details)
A. Schuler

2013

EPSIAE-European Exchange of Higher Education

Cross-Cultural Conference 2013, Steyr, Österreich, 2013, pp. 253-263
M. Gaisch, C. Holzmann, W. Kurschl, H. Mayr, S. Selinger

Architecture and Design of a Generic Device Server for Virtual Reality Hardware Integration in Surgical Navigation

LECTURE NOTES IN COMPUTER SCIENCE, Vol. 8112, No. 1, 2013, pp. 166-173 (Details)
G. Zwettler, W. Backfrieder

GENERIC MODEL-BASED APPLICATION OF MODULAR IMAGE PROCESSING CHAINS FOR MEDICAL 3D DATA ANALYSIS IN CLINICAL RESEARCH AND RADIOGRAPHER TRAINING

Proceedings of The International Workshop on Innovative Simulation for Healthcare IWISH 2013 , Athens, Greece, Griechenland, 2013, pp. 58-64
G. Zwettler, W. Backfrieder

Segmentation by multispectral analysis of MR image data for 3D modelling in surgical planning

Proceedings of The International Workshop on Innovative Simulation for Healthcare IWISH 2013 , Athens, Greece, Griechenland, 2013, pp. 28-33
W. Backfrieder, B. Kerschbaumer, G. Zwettler

Generic 3D Segmentation in Medicine based on a Self-learning Topological Model

Proceedings of the 8th International Conference on Computer Vision Theory and Applications, Barcelona, Spanien, 2013, pp. 104-108
G. Zwettler, W. Backfrieder

Generic Device Server Implementation for Virtual Reality Hardware Integration in Surgical Training

Computer Aided Systems Theory (Eurocast 2013), Las Palmas, Spanien, 2013, pp. 118-119
G. Zwettler, W. Backfrieder

Rule-Based Generation of Mobile User Interfaces

Proceedings of the 10th International Conference on Information Technology: New Generations (ITNG 2013), Las Vegas, Nevada, USA, 2013 (Details)
A. Schuler, B. Franz

Context-enriched personal health monitoring

6. Deutscher AAL-Kongress, Berlin, Deutschland, 2013, pp. 1-7 (Details)
B. Franz, M. Buchmayr, A. Schuler, W. Kurschl

Analysis of Clinical Documents to Enable Semantic Interoperability

Database and Expert Systems Applications, Prague, Tschechische Republik, 2013, pp. 466-473 (Details)
B. Franz, A. Schuler, E. Helm

Regelbasierte Entwicklung von Barrierefreien und Plattformunabhängigen Mobilen Benutzeroberflächen

eHealth2013 – Von der Wissenschaft zur Anwendung und zurück. , Wien, Österreich, 2013, pp. 207-218 (Details)
E. Helm, A. Schuler, H. Mayr

2012

Semantikerhaltung beim Austausch elektronischer Gesundheitsdaten zur Unterstützung der integrierten Versorgung älterer Menschen

Proceedings IV Kongress 2012, Linz, Österreich, 2012
B. Franz

A New Hybrid Algorithm Based on Watershed Method, Confidence Connected Thresholding and Region Merging as Preprocessing for Statistical Classification of General Medical Images

Proceedings of the 24th European Modeling and Simulation Symposium EMSS 2012, Vienna, Österreich, 2012, pp. 73-81
G. Zwettler, W. Backfrieder

Energie- und kostenoptimierter Gebäudeentwurf durch automatische Simulation von Architektur und Materialauswahl

Tagungsband FFH 2012, Graz, Österreich, 2012, pp. 185-189
G. Zwettler, S. Hinterholzer, P. Track, F. Waschaurek, E. Hagmann, R. Woschitz

Mobile Access to Healthcare Monitoring Data for Patients and Medical Personnel

MIE, Quality of Life through Quality of Information, Pisa, Italien, 2012 (Details)
M. Strasser, E. Helm, A. Schuler, M. Fuschlberger, B. Altendorfer

Mobile health solutions for empowered, health-conscious individuals and patients

Proceedings of the 10th International Conference on Information Communication Technologies in Health, Samos, Greece, Griechenland, 2012, pp. 422-432 (Details)
M. Strasser, E. Helm, B. Franz, H. Mayr

Telemonitoring für mobile Pflegedienste: Entwicklung von standardkonformen Schnittstellen

eHealth2012 – Health Informatics meets eHealth – von der Wissenschaft zur Anwendung und zurück, Wien, Österreich, 2012, pp. 179-184 (Details)
M. Strasser, E. Helm, A. Schuler, B. Franz, H. Mayr, C. David

e-Health – Die IT-Basis für eine Integrierte Versorgung: Chancen und Risiken aus technischer Sicht

Proceedings IV Kongress 2012, Linz, Österreich, 2012
H. Mayr, B. Franz

2011

Automatisierte Rekonstruktion klinischer Pfade basierend auf IHE-Integrationsprofilen

eHealth 2011, Wien, Österreich, 2011, pp. 209-214
F. Pfeifer, B. Franz, E. Helm, J. Altmann, B. Aichinger

Providing Semantic Interoperability for Integrated Healthcare Using a Model Transformation Approach

Proccedings of 23rd IEEE European Modeling & Simulation Symposium EMSS 2011, Roma, Italien, 2011, pp. 195-200
B. Franz, H. Mayr

Gyrus and Sulcus Modelling Utilizing a Generic Topography Analysis Strategy for Processing Arbitrarily Oriented 3D Surfaces

Proceedings of the 23rd European Modeling & Simulation Symposium, Rom, Italien, 2011, pp. 111-117
G. Zwettler, W. Backfrieder, R. Pichler

Accelerated Fully 3D Iterative Reconstruction in SPECT

Proceedings of the 23rd European Modeling & Simulation Symposium, Rom, Italien, 2011, pp. 100-104
W. Backfrieder, G. Zwettler

Innovative Klassifizierungskonzepte zur verbesserten quantitativen Diagnostik bei neurodegenerativen Erkrankungen am Beispiel von Alzheimer und Parkinson

Tagungsband FFH 2011 (5. Forschungsforum der österreichischen Fachhochschulen), Wien (Favoriten), Österreich, 2011, pp. 38-41
G. Zwettler, W. Backfrieder, R. Pichler

Towards a Normalized Reference System in Building Construction Planning for Automated Quantitative Assessment and Optimization of Energy and Cost Efficiency

Proc. of the 3rd International ICST Conference on IT Revolutions , Cordoba, Spanien, 2011, pp. 20
G. Zwettler, S. Hinterholzer, P. Track, R. Woschitz, F. Waschaurek, E. Hagmann

Automated Building Construction Design Optimization for Reduction of Construction Costs and Energy Demand

Proceedings of International Conference on Computer Aided Systems Theory EUROCAST 2011, Las Palmas, Spanien, 2011, pp. 233-235
G. Zwettler, S. Hinterholzer, F. Waschaurek, R. Woschitz, E. Hagmann, P. Track

Diagnosis of Neurodegenerative Diseases based on Multi-modal Hemodynamic Classification of the Brain

Proceedings of International Conference on Computer Aided Systems Theory EUROCAST 2011, Las Palmas, Spanien, 2011, pp. 363-365
G. Zwettler, W. Backfrieder, R. Pichler

Providing Technical and Semantic Interoperability for Integrated Healthcare Using an IHE-Compliant System

Proceedings IADIS International Conference e-Health 2011 – EH 2011, Rom, Italien, 2011, pp. 4
B. Franz, H. Mayr

2010

e-Care: mobile Informationssysteme im Gesundheitswesen. Elektronischer Austausch pflegerelevanter Daten zwischen häuslicher Pflege und Gesundheitseinrichtungen

Krankenhaus-IT Journal, Vol. 50, No. 4, 2010, pp. 60-61
B. Franz

Projekt e-Care – Ein Schritt in Richtung Integrierte Versorgung

ÖKZ Das österreichische Gesundheitswesen, Vol. 51, No. 7, 2010, pp. 9-11
B. Franz, M. Lehner, M. Mayr

Harmonizing the Test Support for Object-oriented Legacy Systems Using State-of-the-Art Test Tools

ECOOP 2010 – 1st Workshop on Testing Object-Oriented Software Systems, Maribor, Slowenien, 2010, pp. 9-15
A. Strasser, H. Mayr, T. Naderhirn

BAUOPTIMIZER: MODELLING AND SIMULATION TOOL FOR ENERGY AND COST OPTIMIZATION IN BUILDING CONSTRUCTION PLAN DESIGN

22nd European Modeling and Simulation Symposium EMSS 2010, Fes, Marokko, 2010, pp. 49-58
G. Zwettler, S. Hinterholzer, E. Hagmann, R. Woschitz, P. Track, F. Waschaurek

Functional Segmentation in 3D Angiography

Tagungsband des 4. Forschungsforum der österreichischen Fachhochschulen, Pinkafeld, Österreich, 2010, pp. 79-84
G. Zwettler, W. Backfrieder

e-Care: Austausch von pflegerelevanten Daten zwischen häuslicher Pflege und Gesundheitseinrichtungen

Intelligente Objekte und Mobile Informationssysteme im Gesundheitswesen, Erlangen, Deutschland, 2010
B. Franz, H. Mayr, M. Mayr

IHE-compliant Mobile Application for Integrated Home Healthcare of Elderly People

Proceedings of 7th International Conference on Information Technology : New Generations, Las Vegas, Vereinigte Staaten von Amerika, 2010
B. Franz, H. Mayr, M. Mayr

2009

IHExplorer: Visualisierung, Analyse und Optimierung der Systemintegration im Gesundheitswesen

eHealth2009, Wien, Österreich, 2009, pp. 115-121
J. Altmann, B. FRANZ, D. Mörtenschlag, F. Pfeifer, M. Strasser, B. Aichinger, R. Koller

Collaborative Knowledge Networks Based on IHE

Proceedings of 21st European Modeling and Simulation Symposium EMSS 2009, Tenerife, Spanien, 2009, pp. 3
J. Altmann, F. Pfeifer, M. Strasser, B. Franz, H. Mayr

FAST FULLY-AUTOMATED MODEL-DRIVEN LIVER SEGMENTATION UTILIZING SLICEWISE APPLIED LEVELSETS ON LARGE CT DATA

Proceedings of 21st European Modeling and Simulation Symposium EMSS 2009, Tenerife, Spanien, 2009, pp. 161-166
G. Zwettler, W. Backfrieder, R. Swoboda, F. Pfeifer

Evaluation of a Statistical Shape Model Based Approach for Recovering the 3D LV Shape from Projective X-Ray Images

Proceedings of 21st European Modeling and Simulation Symposium EMSS 2009, Tenerife, Spanien, 2009, pp. 154-160
R. Swoboda, G. Zwettler, J. Scharinger, C. Steinwender, F. Leisch

Realisierung von prozessorientierten Bildverarbeitungsketten auf Basis von ITK, VTK und RCP

Tagungsband des 3. Forschungsforums der österreichischen Fachhochschulen, Fachhochschule Kärnten, Villach, Österreich, 2009, pp. 6
G. Zwettler, W. Backfrieder, R. Swoboda, F. Pfeifer

Phonem-codierte Audiosignal Sprachenerkennung unter Verwendung von Kompressionsalgorithmen

Tagungsband des 3. Forschungsforums der österreichischen Fachhochschulen, Fachhochschule Kärnten, Villach, Österreich, 2009, pp. 2
G. Zwettler, W. Backfrieder

Automatisierte Gefäßbaummodellierung zur funktionalen Analyse von 2D und 3D medizinischen Bilddaten

Master/Diploma Thesis, FH OÖ Fakultät Hagenberg, Österreich, 2009, pp. 104
G. Zwettler

Integrated Care Using a Model-Based Patient Record Data Exchange Platform

Proceedings of 21st European Modeling and Simulation Symposium EMSS 2009, Tenerife, Spanien, 2009, pp. 8
B. Franz, H. Mayr, M. Mayr, F. Pfeifer, J. Altmann, M. Lehner

IHE-Compliant Exchange of Patient Data to Enable Home & Mobile Nursing Care of Elderly People within an e-Care Affinity Domain

Proceedings of the 6th International Conference on Information Technology : New Generations, Las Vegas, Vereinigte Staaten von Amerika, 2009
B. Franz, M. Lehner, H. Mayr, M. Mayr

Integrated Information Exchange of Mobile and Stationary Nursing Care Using an IHE-Compliant IT Infrastructure

Proceedings Med-e-Tel 2009, Global Telemedicine and eHealth Updates: Knowledge Resources Vol. 2, Luxembourg, Luxemburg, 2009, pp. 452-455
H. Mayr, B. Franz

2008

Das Adaptierte Twin-Peaks-Modell zur Ermittlung von iterations- und projektbezogenen Kennzahlen für das Requirement Management

Requirements Days 2008, Nürnberg, Deutschland, 2008, pp. 19
H. Mayr

MIPP: A Medical Image Processing Platform based on ITK, VTK and Eclipse RCP

The Insight Journal, Vol. 3, No. 2, 2008, pp. 36
R. Swoboda, W. Backfrieder, G. Zwettler, F. Pfeifer

Automated segmentation of liver morphology for diagnosis and surgery planning

IGRT Vienna 2008 , Wien, Österreich, 2008, pp. 14
W. Backfrieder, G. Zwettler, R. Swoboda, F. Pfeifer, H. Kratochwill, F. Fellner

Fast Skeletonization for Medial Axis Extraction on Tubular Large 3D Data

Challenges in Biosciences: Image Analysis and Pattern Recognition Aspects, St. Magdalena, Linz, Austria, Österreich, 2008, pp. 91-102
G. Zwettler, W. Backfrieder, F. Pfeifer, R. Swoboda

Modellbasierter Ansatz zur robusten und automatisierten Segmentierung der Leber aus hochauflösenden CT Daten

Proceedings of FFH2008 Fachhochschul Forschungs Forum, Wels, Österreich, 2008, pp. 253-259
G. Zwettler, W. Backfrieder, F. Pfeifer, R. Swoboda, H. Kratochwill, F. Fellner

Rapid Prototyping in Surgery: Eine Anwendung zur Planung von Kieferorthopädischen Eingriffen

Proceedings 2009 Tagungsband Bericht 2008 Journal Tagungsband – 6 – of FFH2008 Fachhochschul Forschungs Forum, Wels, Österreich, 2008, pp. 2
F. Pfeifer, W. Backfrieder, G. Zwettler, R. Swoboda, H. Kratochwill, M. Malek, R. Hainisch

Accelerated Skeletonization Algorithm for Tubular Structures in Large Datasets by Randomized Erosion

Proceedings of the 3rd International Conference on Computer Vision Theory and Applications, Funchal, Madeira – Portugal, Portugal, 2008, pp. 74-80
G. Zwettler, W. Backfrieder, F. Pfeifer, R. Swoboda

Self-Learning Navigation Maps Based Upon Data-Driven Models Using Recorded Heterogeneous GPS Tracks

Proceedings of the 20th European Modeling and Simulation Symposium, Campora S. Giovanni, Italien, 2008
C. Novak, B. Franz, H. Mayr, M. Vesely

Incremental Navigation Map Enhancement with GPS Tracks from Heterogeneous Sources

Proceedings of The 2008 Internationa Conference on Machine Learning; Models, Technologies and Applications, Las Vegas, Vereinigte Staaten von Amerika, 2008, pp. 787-793
M. Vesely, C. Novak, A. Reh, H. Mayr

Effiziente Management-Information bei agilen Vorgangsweisen – ein Widerspruch?

Proc. 23. STEV-Österreich-Fachtagung IT-/Software-Qualitätsmanagement in der Praxis, Wien, Österreich, 2008, pp. 48-59
H. Mayr

I-Navigate P&R: Erweiterung einer Automobil-Navigation um Online- Park&Ride-Funktionalität

Proceedings of FFH2008 Fachhochschul Forschungs Forum, Wels, Österreich, 2008, pp. 3
J. Altmann, H. Mayr, W. Steinbichl

2007

Value-based Software Engineering Using the Adapted Twin-Peaks Model

Proceedings of International Mediterranean Modelling Multiconference I3M2007, Genoa, Italien, 2007, pp. 289-293
H. Mayr

I-Navigate: Selbstlernende Navigationskarten für tagesaktuelle, personalisierte Navigation im Fahrzeug und zu Fuß

Tagungsband des ersten Forschungsforum der österreichischen Fachhochschulen, Fachhochschule Salzburg, Campus Urstein, Österreich, 2007, pp. 244-250
H. Mayr, M. Vesely

I-Navigate: Intelligent, Self-adapting Navigation Maps

Proc. 14th IEEE International Conference and Workshop on the Engineering of Computer Based Systems (ECBS’ 07), Tucson, Vereinigte Staaten von Amerika, 2007, pp. 397-402
H. Mayr

Using Self-adapting Navigation Data for Intelligent, Personalized Vehicle Guidance

Proceedings of International Conference Computer Aided Systems Theory EUROCAST 2007, Las Palmas, Spanien, 2007, pp. 1097-1104
M. Vesely, H. Mayr

Towards automated segmentation and classification of liver lesions for diagnosis and surgery planning

International Journal of Computer Assisted Radiology and Surgery, Berlin, Deutschland, 2007, pp. 460-461
W. Backfrieder, G. Zwettler, R. Swoboda, F. Pfeifer, H. Kratochwill, F. Fellner

Automatische Klassifizierung der Leber aus hochauflösenden Multi-Slice CT Daten

Tagungsband des ersten Forschungsforum der österreichischen Fachhochschulen, Fachhochschule Salzburg, Campus Urstein, Österreich, 2007, pp. 425-426
G. Zwettler, W. Backfrieder, R. Swoboda, F. Pfeifer, H. Kratochwill, F. Fellner

Rapid Prototyping in Surgery

Tagungsband des ersten Forschungsforum der österreichischen Fachhochschulen, Fachhochschule Salzburg, Campus Urstein, Österreich, 2007, pp. 401-402
F. Pfeifer, W. Backfrieder, R. Swoboda, G. Zwettler, H. Kratochwill, F. Fellner, M. Malek, R. Hainisch

2006

RAPS

Proceedings FH Science Day 2006, Hagenberg, Österreich, 2006, pp. 74-80
F. Pfeifer, W. Backfrieder, R. Swoboda, G. Zwettler

Segmentation of Coronary Arteries in Cine Angiography

Proceedings of the International Mediterranean Modelling Multiconference (I3M 2015), Barcelona, Spanien, 2006, pp. 675-680
G. Zwettler, R. Swoboda, W. Backfrieder, C. Steinwender, F. Leisch, C. Gabriel

2005

Application of 3D and 4D Heart Models in Cardiac Therapy Planning

Proceedings of Conceptual Modeling and Simulation Conference (CMS 2005), Marseille, Frankreich, 2005, pp. 185-191
R. Swoboda, W. Backfrieder, G. Zwettler, M. Carpella, C. Steinwender, F. Leisch, C. Gabriel

Algorithmen zur Koronargefäßsegmentierung in der Cine-Kardioangiography

Master/Diploma Thesis, FH OÖ Fakultät Hagenberg, Österreich, 2005, pp. 94
G. Zwettler

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Forschungsgruppe AIST

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Fachhochschule Oberösterreich

Researchgroup AIST

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University of Applied Sciences Upper Austria

University of Applied Sciences
Upper Austria